Note that in a dependency tree, a path between two arguments must pass through their common predicate.

(3) Estimating η using maximum likelihood over the development set, which results in η ∼ 0.1 (λ ∼ 2.3), corresponding to the edge density P(I uv = 1) ∼ 0.09.


From an empirical point of view, we conduct many new experiments that examine both the local entailment classifier as well as the global algorithm.

in B we see that ILP-Global takes into consideration the global interaction between the four nodes and other nodes of the graph, and decides to split this strongly connected component in two, which improves the precision of ILP-Global.


The text T is said to entail the hypothesis H if, typically, a human reading T would infer that H is most likely true (Dagan et al.

we turn to existing knowledge resources that were created using both manual and automatic methods, expressing various types of linguistic and statistical information that is relevant for entailment prediction: WordNet: contains manually annotated relations such as hypernymy, synonymy, antonymy, derivation, and entailment.


The Minimal Distance constraint relates the choice of the given value V G to the choices made for the relation and the scale.

in general this kind of procedure can lead to computational inefficiency; in planning descriptions of proportions, however, the number of candidates should not be large enough for this to be a problem, given that most combinations will already have been eliminated by the constraints applied before labeling.


This approach receives some support from the peaks in Table 2 for 25% and 75%, which would correspond to points along the S 4 scale.

also, as implied by Grice's (1975) quality and quantity maxims, the benefits of precision need to be balanced against the costs; this trade-off has been discussed within the framework of optimality theory (Dekker and van Rooy 2000;Krifka 2002;Blutner and Zeevat 2011).


We formalize the detection of similar sentences and the projection of role annotations in graph-theoretic terms by conceptualizing the similarity between labeled and unlabeled sentences as a graph alignment problem.

the graph alignment algorithm is worth the added work as the projection of annotations contributes to achieving better SRL results.


In this situation, neither "the cup" nor "the table" is distinguishing, but "the cup on the table" succeeds in identifying one of the two cups.

the dominant method at the moment is, accordingly, to measure the similarity between generated expressions and those in a suitable corpus of referring expressions.


The IA, by contrast, uses a different expand-method, each time creating a single new state in accordance with the pre-determined preference order.

as NLG is starting to be applied in large-scale applications, the ability to capture generalizations of this kind is bound to become increasingly important.


the level of information expressing the commitment of relevant sources towards the factual nature of eventualities in text.

there were no acute changes, but she did have a 50 pound weight loss.


2006) and a subset of those in the AQUAINT TimeML Corpus.

we applied the one-sample version of the t-test because De Facto's performance results do not conform a distribution, because they were obtained from running the system once over the evaluation subcorpus.


For example, the visit to the zoo with Max in April, Ivet in August, and Arlet in December are three separate events, given the difference in participants and temporal location.

lexical entries structure their factuality information as illustrated in Tables 4 and 5 (for SIPs and NSIPs, respectively).


Hence, we decided to generate two different models: the author level model, in which the factuality of events is assessed relative to the author of the text (i.e., at the level of source s 0 ), and the top source level model, in which event factuality is assigned according to the source with a higher level of nesting in the set of relevant sources for that event (e.g., s m s j s 0 ).

in all other cases, the polarity contributed by the marker will interact with that from the context as specified in Table 2.


The model has been implemented into De Facto, a tool that takes dependency trees as input and returns the factuality profiles of events in text.

by using the SIP say (Example (17b)) the anchor remains uncommitted.


features 16-19 were added to convey information on the top-level sources as well.


Three colleagues provided the normalizations of the sentences following Saurí's guidelines, and the data were then annotated using Mechanical Turk, as described in Section 2.

the goal of this section is to uncover those differences.


As in FactBank, antecedents of conditionals (7), generic sentences (8), and clear cases of uncertainty with respect to the future (9) were tagged Uu by a majority of Turkers.

it emerged last night that a reporter from London Weekend Television unwittingly tipped off residents about the raid when he phoned contacts on the estate to ask if there had been a raid-before it had actually happened.


Comparing the two domain adaptation procedures DA/CUE and DA/ALL, adaptation via transferring only source sentences that contain a target domain cue is, on average, comparable to transferring all the data from the source domain.

this mechanism coincides with our initial goal of using domain adaptation to learn better semantic models.


The common point in these approaches is that in the case of uncertainty, the truth value/reliability of the proposition cannot be decided because some other piece of information is missing.

the condition class is underrepresented in both the source and target data sets and hence no reliable observations can be made regarding this subclass (see table 2).


The adaptation of an uncertainty cue detector constructed for encyclopedia texts requires the special treatment of investigation cues, however, if, for instance, scientific discourse is the target genre since they are underrepresented in the source genre.

it was marked as a cue when trained on biological texts since in this case, there were insufficient training examples of may not being a cue: (16) "Well may we say 'God save the Queen,' for nothing will save the republic," outraged monarchist delegate David Mitchell said.


When an ERG parse is not available we simply revert to the prediction of the dependency rules.

these instances seem a prime source of inspiration for further improvements to the ranker in future work.


We therefore repeated our examination of the effects of using the best-ranked parses for training and testing the ranker.

we investigated the effects of combining their predictions.


A major annotation effort for temporal and event expressions is the TimeML specification language, which has been developed in the context of reasoning for question answering (Saurí, Verhagen, and Pustejovsky 2006).

the correct (embedded) target found is the head of the syntactic complement of be.


We used a version of the parser that produces flattened trees.

the name of the semantic tag is grafted onto the new node and becomes its category label.


Generic symbols in translation rules (i.e., the non-terminal symbol "X") were replaced with structured information at multiple levels of abstraction, using a tree-grafting approach that we describe subsequently.

the LDOCE code I associated with the verb hunger was hand-changed to I-FOR.


For somebody with only a casual interest in the field, it may not be didactic enough.

morgan & Claypool (Synthesis Lectures on Human Language Technologies, edited by Graeme Hirst, volume 12), 2011, ix+101 pp; paperbound, ISBN 978-1-60845-707-6, $40.00; ebook, ISBN 978-1-60845-708-3, $30.00 or by subscription This short volume gives an introduction and overview of current techniques for learning to rank objects.


He was disappointed, however, that most of the linguists he hired were more interested in pursuing their theoretical studies than in tackling the practical problems of MT.

yngve, along with many others at the time, deprecated the premature publicity around the Georgetown-IBM system demonstrated in January 1954.


They decided that what was needed was a programming system that accepted notations of a kind familiar to linguists, rather than systems such as FORTRAN and COBOL designed for mathematicians and business applications.

yngve decided to investigate error correcting properties of language (yngve 1954).


Such models can also be expected to improve reference resolution: If better predictions can be made about what people will say in a given situation, automatic speech recognition language models can be tighter, NLP grammars can be smaller, and unlikely parses can be avoided, improving both speed and accuracy.

it would be interesting for future work to extend Fruit Carts-style domains to more realistic object construction and placement tasks.


We therefore have We give a bound on |Z 1 |, the number of "high probability" derivations.

the distribution-free setting, however, is not ideal for analysis of natural language, as it has to account for pathological cases of distributions that generate data.


2004) and ordering sentences (Barzilay, Elhadad, and McKeown 2002) in order to make the summary more coherent.

although some fundamental concepts of the theory, such as the ranking of entities in an utterance, aren't explicitly specified, various researchers have applied centering theory to language generation (Kibble and Power 2004;Karamanis et al.


The Same Operator is then applied to all possible pairs of trees in each candidate forest.

at the same time, a reader could spend much more time examining the graphic to further investigate something of interest or something they noticed during their casual glance.


The ideal measurement axis descriptor of the bar chart (Yahoo's registered users) consists of the core registered users (appearing in the Description) augmented with the proper noun Yahoo that appears in the Overall Description.

the top-down planning approach does not appear to fit.


Because our text plans are significantly more constrained, it is possible to enumerate each of them and choose the one that best fits our rating criteria.

some propositions require more than one predicate.


The instructions for the judges included information about all linguistic characteristics discussed in Section 3, including syntactic and semantic characteristics.

in the first model, polysemous words are assigned to one single, independent class, whereas in the second they are assigned to each of the two basic classes that give rise to the regular polysemy.


This model is more adequate than the first one, as it accounts for the properties of the basic classes found in polysemous words, but it fails to account for the differences between polysemous and monosemous words.

adjective classes could be further exploited in WSD in at least two respects: (1) to establish an inventory of adjective senses (if polysemous instances are correctly detected; this is where sense induction and our own work fits in), and (2) to exploit class-based properties for the disambiguation, similar to related work on verb classes (Resnik 1993;Prescher, Riezler, and Rooth 2000;Kohomban and Lee 2005).


Bohnet, Klatt, and Wanner (2002) also has similar goals to the present research, as it is aimed at automatically classifying German adjectives.

we first model polysemy in terms of independent classes to be separately acquired (e.g., an adjective with two senses a i and b i belongs to a class AB defined independently of classes A and B), and show that this model is not adequate.


The topic of large-scale distributed language models is relatively new, and existing work is restricted to n-grams only (Zhang, Hildebrand, and Vogel 2006;Brants et al.


The permutation of any sentential form of an SCFG of rank r can be produced by composing permutations of length no greater than r, by induction over the length of the derivation.

now we consider how further rules in the LCFRS derivation make use of the tuple t 0 , t 1 , .


The correspondence between LCFRS string tuples and MBOT tree yields gives us our first result: From a given MBOT, construct an LCFRS as described previously.

for all i, 1 ≤ p(i) ≤ n. Given this notation, the LCFRS rule corresponding to the MBOT rule l → r is constructed as S 0 → g(S 1 , S 2 , .


t Σ (X) is the smallest set such that X ⊂ t Σ (X) and σ(t 1 , .


Similarly, in a predicate-argument formula, there is nothing meaningful about being the first or second or third item in a list.

the basic textbooks in beginning linguistics classes at Michigan typically provided step-by-step procedures for going from data to units, so this movement was well-supported there.


But during much of my childhood I was convinced that I personally had difficulties with language: The symptom was that I could never think of anything to say.


For example, the well-known Ratnaparkhi (1999) parser used a part-of-speech (POS)-tagger and a finite-state noun phrase (NP) chunker as initial stages of a multi-stage Maximum Entropy parser.

pipeline systems make use of simpler models with more efficient inference to reduce the search space of the full model.


The quadratic bound does not include any potential reduction of work for the remaining open cells, however.

these begin and end constraints can be extracted from the output of the chunker, but the chunker is most often trained to optimize chunking accuracy, not parsing accuracy (or parsing precision).


w e for some e > b and w b ∈ B otherwise.

an overly constrained linearbounded parse will favor one branching direction over the other.


We call this approach "linear constraints."

we believe the dynamic programming chart could be constrained even further if we take into account the population and structure of chart cells at a finer level.


Graphically: There exist two constraints on possible assignments of candidates to positions.

we hypothesized that this difference might indicate the presence of approximately one implicit argument per predicate instance.


In Example (4), Jill becomes injured whereas in Example (6) she is quite happy.

to help prevent data sparseness, the size of the group was small, and the predicates were carefully chosen to maximize the observed frequency of implicit argumentation.


(11) He added that the company has been manufacturing carpet since 1967.

for a missing argument position iarg n , the student's annotation agreed with our own if both identified the same implicit argument or both left iarg n unfilled.


Our previous study used fixed partitions of training, development, and testing data.

the crossvalidated approach reported in this article alleviated both problems.


associated with a NomBank or PropBank predicate.

in general, the nominal SRL problem is formulated as follows: Given a predicate that is annotated in NomBank as bearing arguments, identify these arguments within the clause or sentence that contains the predicate.


This was done to maintain compatibility with these and other resources.

in order to penalize low-frequency observations with artificially high scores, we used the simple discounting method described by Pantel and Ravichandran (2004) resulting in the following modification of Equation 3: if two argument positions are rarely observed as coreferent, the value x x+1 will be small, reducing the PMi score.


Then, we combine these models by including them as different feature functions.

haAretz is published in hebrew, but portions of it are translated to English.


Sense-based models use the information from the AnCora-Nom-R3 lexicon at sense level, that is, the features for learning (and classification) are associated with the specific senses.

closely related to SRL is the task of learning Selectional Restrictions for a predicate, for example, the kind of semantic class each argument of the predicate must belong to (Mechura 2008).


This is due to the fact that result nominalizations can denote an object whereas verbs cannot denote objects.

the data seem to confirm Picallo's and Grimshaw's hypothesis.


Many language classes are closed under intersection with a regular set, and the result of the intersection of a language L(G) with a regular language α is describable as a specialization G α of G that assigns to all and only the strings in α effectively the same parse trees as G would assign.

the rules in (26a) result from reducing the licensing rules of the derivation in Figure 4 with the reducing substitution (20).


Thus the set of constants needed to correctly reduce an arbitrary description as a derivation proceeds incrementally may be larger than the number of elements in the input f-structure and larger than what is required for an equivalent description for a complete derivation.

iRD F can be determined without knowing the details of the valid derivations for F, just on the basis of F and the LFG grammar G alone.


So far we imposed only loose restrictions on the ingredients of the generation grammar G F , and a faithful implementation of the grammar definition may create categories and rules that are either useless or redundant.

the depicted LFG derivation and the context-free derivation are also related by the construction of the second half of the proof.


The analysis of a sentence then consists of mapping the string of words into a string of morphemes to which the LFG grammar is then applied.

3 the ↓ of the annotations on a daughter category of a rule and the ↑ of the annotations of the rule that further expands that category are always instantiated with the same node.


After years of being ignored by researchers in other areas of computational linguistics and language technology, many of these same researchers are beginning to think that their own work could benefit from treating text as more than just a bag of sentences.

stede then links the current chapter to the previous one through a discussion of entity-based coherence (section 3.3) and then discusses how to identify when a pronoun or definite noun phrase should be treated as anaphoric (section 3.4) as groundwork for discussion of anaphora resolution (sections 3.5-3.7).


Stede justifies inclusion of this chapter for two reasons-the close connection between coreference resolution and topic segmentation and the benefits to text analysis provided by having its pronouns resolved.

they are beginning to think that discourse offers some low-hanging fruit-achievable improvements in system performance that exploit either aspects of text structure or the context that text establishes and uses for efficient referring and/or predicational expressions.


Nevertheless, it is worth noting that, for individual languages, differences in scores can be quite substantial.

hence, an early commitment to root attachment is more likely to be correct in this case, even if it is more error prone in general.


Although the overall patterns emerging when averaging over many data sets can largely be explained in this way, there is also considerable variation across data sets that we do not yet fully understand, however.

we show that a greedy, left-to-right, arc-eager transition-based parser consistently performs worse when the dummy root node is placed at the beginning of the sentence (following the current convention in data-driven dependency parsing) than when it is placed at the end or omitted completely.


2009) is a pipeline approach where POS and morphological information is predicted as a preprocessing step to the actual parsing.

due to its mostly unambiguous case system, we find a much smaller effect for Hungarian.


We rerank 100-best lists from BitPar (Schmid 2004), which uses the grammar extraction procedure and lexical resources introduced in Section 3.

in this case, the grammatical function of the NP is PNC.


In general, these results indicate that these two parsers are competitive.

we use the German finite-state morphology SMOR (Schmid, Fitschen, and Heid 2004) to provide sets of possible POS tags for all words.


We also hope that our work can serve as a cookbook of ideas to try for others working on parsers for other morphologically rich languages.

using our 16 rich features (compared with 117,000 sparse features) is more suitable for the settings where only a limited amount of training instances are available (the training sets consist of 567 sentences of Europarl in seven-fold cross-validation).


r On the side of the probabilistic model, there are certain independence assumptions made in our model that are too strong.

the LCFRS rule is S(XYZ) → VP(X, Z) VMFIN(Y).


The respective rightmost graphs in Figures 26 and 27 show the average number of items produced by the parser and the parsing times for different sentence lengths.

we can reorder the right-hand side of a rule before binarizing it.


In addition, whereas English has a large treebank, containing over one million annotated words, many other languages have much smaller treebanks, which also contribute to the drop in the accuracies of the data-driven parsers.

the analysis revealed the following reasons for the low impact of the agreement filter: (1) the grammar is strong enough to produce fairly accurate structures, which have very few agreement mistakes to begin with, and 2fixing an agreement mistake does not necessarily mean fixing the entire parse-in some cases it is very easy for the parser to fix the agreement mistake and still produce an incorrect parse for other parts of the structure.


This is most notably apparent in verbal phrases and sentential levels.

the 1-best trees of the 480 sentences of the development set contain 22,500 parsetree nodes.


The splits are evaluated based on an information gain criteria, and splits that are not useful are merged back into their parent symbol, resulting in a smaller grammar (if the symbols B 1 and B 2 are merged back into B, the rules A → B 1 C and A → B 2 C are merged into A → B C).

the tagging probabilities do not depend on the vocabulary size, allowing a very simple combination.


Aspects of the work presented in this article are discussed in earlier publications.

by annotating the symbols as follows: the grammar now forces the VP to be produced before the NP, but still allows the NP to be dropped.


Presumably, this is because of its lower prediction accuracy.

cATiB's dependency representation is based on traditional Arabic grammar and emphasizes syntactic case relations.


a PTSG is a 5-tuple V, Σ, R, ♦, θ where c ∈ V are non-terminals; t ∈ Σ are terminals; e ∈ R are elementary trees; 5 ♦ ∈ V is a unique start symbol; and θ c,e ∈ θ are parameters for each tree fragment.


2011), in which we used the basic FTB tag set.

fTB-UC loses information about MWEs with non-compositional semantics, however.


When we take a closer look at the 18 instances where our model correctly identifies the NE alignments and Maxent fails to do so on the test set, we find that the internal component alignment-pair feature in the Maxent approach to be dominant in causing 56% of the errors (10 out of 18).

as an example in Table 12, the corresponding anchor of the Chinese NE "东 协" (aSEaN) is not initially identified.


Furthermore, each selected substring included the Chinese bigrams that appear more frequently than those within the remaining sub-string (of its reference CNE).

english Ne boundaries are easier to identify with explicit words and capitalization clues.


The tagging cost is trained on the tagging result of Training Set I by the initial NE detection system.

it is important to have a good NE recognizer that has been well trained.


the basic idea is to use the derivations of the grammar to guess truth assignments for the variables, and to use the feature of unbounded fan-out to ensure that the truth assignment satisfies all clauses.


Logical form: answer(A,(job(A),language(A,'java'),¬company(A,'IBM'))) The JOBS data set comes with a database, which we can use as the world w. When the logical forms are evaluated on this database, however, close to half of the answers are empty (no jobs match the requested criteria).

we consider any a i that occurs in column 1 of the arrays of this base denotation ([a i ] ∈ σ i .b.A[1]).


The operation is parametrized by a sequence of distinct indices i that specifies the order in which the columns should be processed.

across all utterances, there are 388 word types, and the length of an utterance ranges from 2 to 23 words, with an average of 9.8 words.


Like our work, it also uses database type constraints to rule out spurious logical forms.

far, we have focused on DCS trees that only use join relations, which are insufficient for capturing higher-order phenomena in language.


Superlatives use the argmax and argmin predicates, which are defined in Section 2.3.

we iteratively scan through the training examples in a random order.


Systematically describing these "crosscutting" constructions and their processing, especially in a way that scales to large data encompassing both form and meaning and accommodates both parsing and generation, would in our view make for a more comprehensive account of language processing than our field is able to offer today.

this chapter is of a tutorial nature, illustrating the step-by-step application of four kinds of noun phrase constructions to expand transient structures in processing.


The book is unusually clear and honest in highlighting limitations in the current understanding of crucially relevant concepts, including existing formalization techniques.

interpreting motion, therefore, turns out to be one of the most fundamental research issues for a variety of purposes both in basic (or cognitive) and applied (or computational) research.


The paraphrase pairs come with their equivalent parts manually aligned (Cohn, Callison-Burch, and Lapata 2008).

the phenomenon of paraphrases, as understood in linguistics, is difficult to characterize.


Given the example sentence, we will thus add a feature lex pol:positive because of the word easy, which is listed as positive in the sentiment lexicon.

both baselines are outperformed by the reranker using holder interaction features, which we refer to as the expression/holder reranker (EHR); the differences to the strong baseline in recall and F-measure are both statistically significant (p < 0.0001).


We will refer to all word pairs that satisfy either of the two properties to some degree as contrasting.

rule 1 (high confidence for opposites): If the words in a pair occur in adjacent thesaurus categories, then they are marked as opposites.


If something is musical, would you assume it is not dissonant, and vice versa?

for each word pair from the list of WordNet opposites, we chose one word randomly to be the target word, and the other as one of its candidate options.


(2003) and Turney (2011), we can also refer to these results simply as accuracy.

it marked all words in TURN as synonyms.


Section 6 presents our method to automatically compute the degree of contrast between word pairs by relying on the contrast hypothesis, the degree of contrast hypothesis, seed opposites, and the structure of a Roget-like thesaurus.

even though a high tendency to co-occur is a feature of highly contrasting pairs, it is not a sufficient condition for detecting them.


Note that this method of determining contrasting categories does not explicitly identify a seed opposite, but one can assume the head words of these category pairs as the seed opposites.

ascent and descent occur in prime contrasting thesaurus paragraphs.


Before discussing this, let us first see how such tuples are processed by XMG.

thirdly, XMG is extensible in that its computational architecture facilitates the extension to other linguistic formalisms.


These principles depend either on the dimension (e.g., scope constraints in flat semantics), the target formalism (e.g.

for instance, the semantic index s in the tree for runs is unified during substitution with the semantic index j labeling the root node of the tree for John.


In the PropBank training corpus, however, the argument following the verb fail is labeled Arg2 in 79% of the cases.

the results of the SP models in laboratory conditions are presented in Section 4.


Taxonomy induction is the main theoretical contribution of the paper.

second, we disconnect "false" leaves, namely, leaf nodes which are not in the initial terminology and with no outgoing edges (e.g., output in Figure 3a).


Input: a graph G q = (V, E) to be clustered Output: a clustering C of the vertices in V class(v i ) := i 3: Dice (u, v) 9: 10: As soon as an iteration produces no change in the clustering (line 11), the algorithm stops and outputs the final clustering (line 12).

to the best of our knowledge, with the exception of some very preliminary results (Véronis 2004;Basile, Caputo, and Semeraro 2009), this is the first time that unsupervised text understanding techniques have been shown to considerably boost an Information Retrieval task in a solid evaluation framework.


Then, a maximum spanning tree T q of the graph is calculated starting from vertex q (see the bold edges in Figure 4(e)).

this experiment corroborates the findings obtained from our extrinsic experiments: Curvature is the worst-ranking system (probably because of the low number of induced senses), whereas HyperLex and B-MSt are more apt to discriminate between the meanings of an input query.


Finally, because we are interested in the real-world application of the WSI techniques we discussed, we decided to collect statistics about the execution times of each system on the AMBIENT and MORESQUE data sets.

text snippets with no word in common tend to be clustered separately even if they share the same meaning, whereas snippets with words in common may be grouped together even if they refer to different meanings of the input query.


We manually annotated the bigrams with the correct syntactic dependencies (in the Stanford collapsed typed dependency format) and compared these with the syntactic relations expressed in the linguistic phrases.

they both found that adding syntactic phrases to words improves classification accuracy slightly.


The parser lexicon was compiled from different technical terminologies, such as the SPECIALIST lexicon and the UMLS.

the parsers will output fewer but more variable tokens, which results in lower token/type ratios and a lower impact of lemmatization.


A good index is crucial in a book like this, also because it helps to fulfill the aim of showing the reader how the underlying techniques recur across applications.

an example language tutoring system for learners of Portuguese is then addressed.


for numerous terms such as n-gram, part-of-speech tagging, parsing, supervised learning, and so forth, many (sometimes a majority) of the occurrences in the book are not covered by the index.


States e 1 and e 2 are not the same unless there is an intermediate state.

my thesis was on Earley's algorithm applied to natural language.


the intent is to capture the information that can be conveyed by adjacency.


As already noted, planarity is a very mild relaxation of the better known projectivity constraint, the only difference being that planarity allows graphs with covered roots (see Definition 2), and studies of natural language treebanks have shown the vast majority of non-projective structures to be non-planar as well (Kuhlmann and Nivre 2006;Havelka 2007).

the parser by Goldberg and Elhadad (2010) can be seen as an implementation of this transition system, which uses a training algorithm that assigns a weight to each of the ATTACH-RIGHT(i) EF and ATTACH-LEFT(i) EF transitions in such a way that "easier" (more reliable) attachments are performed first.


; e q , resulting from composing the elementary transitions in the chain.

any graph that meets the SINGLE-HEAD constraint and can be obtained using the S P transition system (which has been proven complete) can also be generated by this one.


We will say that the length of an arc (i, j) is (i, j) = max(i, j) − min(i, j).

if the left endpoints of the arcs in A 1 n+1 are i 1 , i 2 , .


An independent classification of (A. Smith, he) and (A. Smith, she) produces contradictions.

a (hyper)graph is then generated using the applied constraints and their weights.


The version of RELAXCOR used in SemEval had a balance value fixed to 0.5, which proved to be an inadequate value.

in case that after that a support is higher than 1 or −1 then its value is cutted to 1 or −1, respectively.


The organization is scheduled to meet in Vienna... Google Inc. is offering new applications...

an experimental approach in this field needs the expressiveness of the entity-mention model as well as the mention-pair model in order to use the most typical mention-pair features.


For example, animacy enables the linking of firms from Taiwan and they, and avoids the linking of 17 year and she.

in the mention detection step, the system extracts mentions by inspecting all noun phrases (NP) and other modifier pronouns (PRP) (see Section 3.1 for details).


To understand the errors in the system, we analyzed and categorized them into five distinct groups.

for each mention under consideration, we select the first antecedent that matches any of the available sieves.


Nevertheless, plagiarism detection aims at retrieving specific (plagiarized-source) fragments rather than documents.

this grouping allows for the analysis of detectors' performance in Section 5.2.


The general form of ISBN models that have been proposed for modeling derivations of structures is illustrated in Figure 9.

previous work on joint phrasestructure parsing and semantic role labeling also suggests that joint models of these two tasks can achieve competitive results when latent representations are induced to inform both tasks, as shown in Musillo and Merlo (2006) and Merlo and Musillo (2008).


Most of these approaches not only learn syntactic and semantic representations independently, but also use pipelines at testing time.

tends to result in more parameters to train, including larger vocabulary sizes.


They propose a deep learning architecture to solve a task closely related to semantic role labeling.

for every partial analysis, we consider only K 2 possible next actions.


The efficiency of the model is discussed in more detail in Section 6.5.

our model connecting the latent vectors within the semantic layer significantly improves both the precision and the recall of the predicted propositions over the model where these connections are removed (second vs. third line).


This simplified scheme handles the different ways in which substitution and adjunction can be applied, but treats prediction trees like any other elementary tree.

the function verify tries to establish a correspondence between the k-marked node visits in F 1 and the node visits in G 1 .


We treat participants as a random factor, which means that our models contain an intercept term for each participant, representing the individual differences in the rates at which they read.

adjoining into a node that carries markers pushes the two markers apart.


Finally, we discuss how POStags can be used for count features.

the most common type of compound is the noun+noun compound, which makes up 62% of the compounds in the German news corpus of Baroni, Matiasek, and trost.


1982), but in the context of developing and implementing analyses, Sag and colleagues added innovations to the underlying theory until its formal basis was so different it warranted a new name.

he argued that the unacceptability associated with phenomena such as "island constraints" derives not from ungrammaticality but in fact from more general processing pressures (Staum and Sag 2008;Hofmeister and Sag 2010;Staum Casasanto, Hofmeister, and Sag 2010;Hofmeister et al.


The exact algorithms are slower than the LP versions, but compared with CPLEX, AD 3 is significantly faster and has a narrower gap between its exact and LP versions.

2009), question answering (Narayanan and Harabagiu 2004;Shen and Lapata 2007), and paraphrase recognition (Padó and Erk 2005).


An experimental study on unsupervised graph-based word sense disambiguation.

in order to factor out the difference in the WordNet version, we performed experiments using WN2.1 and eXtended WordNet, yielding 58.7 and 56.5 F1 for S2AW and S3AW, respectively.


Figure 2 shows a graphical model of an HMM.

4 As is standard, we use two kinds of feature functions: transition and observation.


Our results show that IE based on distributional similarity tends to improve as the quality of the latent variable model used to measure distributional similarity improves.

we believe the posterior distribution over latent states given a word type is more informative in our HMM model than the PL-MRF model.


the page count should be based on a one-column format with up to about 700 words per page.


A similar scenario would arise with many other NLP tasks, such as parsing or speech recognition, where dialectal content would be needed in large quantities for adequate training.

table 6 summarizes the accuracy rates for these models, and includes rates for the baselines that do not utilize the dialect-annotated data.


(For the latter kind of models, the accuracy rates reported are based on a training set size of 90% of the available data.)

dialect→dialect confusion constitutes a smaller proportion of errors than dialect→MSA confusion.


Our proposal distribution samples each variable with uniform probability working top-down through the forest.

in general, algorithms that match all possible rules are likely to be prohibitively slow, as well as unwieldy to implement.


If we choose an incoming hyperedge according to the probability P t (τ(z)) of the resulting tree, holding all other variable assignments fixed, we see that, because P t is uniform, we will choose with uniform probability of 1/m among the m incoming hyperedges at each node.

the size of the grammar taken from the single sample (100th sample) is only about 9% of the baseline but still produces translation results that are not far worse than the baseline.


This also makes our grammar weakly equivalent to an inversion transduction grammar (Wu 1997), although the conversion would create a very large number of new nonterminal symbols.

note that the τ(s j )'s only differ at nodes below n. Let z[in(n)] be the vector consisting of the variables at nodes below n (that is, contained in subtrees rooted at n, or "inside" n) in the forest, and let z[in(n)] be the vector consisting of variables not under node n. the vectors z[in(n)] and z[in(n)] partition the complete vector z.


Note first that we can perform at most k − 1 Unshift transitions moving a word back to the buffer (because a word can only be moved back to the buffer if it has no head, which can only happen once since Shift is now forbidden).

the new system, shown in Figure 3, differs in four ways from the old system: Configurations are extended with a boolean variable e, keeping track of whether we have seen the end of the input, that is, whether we have passed through a configuration with an empty buffer.


2010introduced "fictitious" authors, adding a unique "author" to each document.

the ranking of methods on PAN'11 is similar to the ranking on the Judgment data set, though on Judgment the difference between LDA-SVM and token SVM is much smaller.


Recently, rating prediction algorithms that are based on matrix factorization have become increasingly popular, due to their high accuracy and scalability (Koren, Bell, and Volinsky 2009).

, 2010: where X X X −di and Z Z Z −di are all the author and topic assignments, respectively, excluding the assignment for the i-th word of the d-th document; c (AT) at is the count of topic t assignments to author a; and c is the count of word w di in author topic t. Here, all the counts exclude the di-th assignments (i.e., x di and z di ).


For all the other data sets we utilized ten-fold cross validation.

the number of author topics T (A) can be different from the number of document topics T (D) , which enables us to vary the number of author and document topics according to the number of authors and documents in the corpus.


by each of the 18 reviewers that were listed in the proceedings, which resulted in a training corpus of 171 documents with 196 authors overall (some of the reviewers have co-authored papers with other reviewers).

aT is also limited in that it does not model documents.


This is because, in order to sample the event indices into the set of indicator random variables Z, the HDP models need to store in memory large matrices that encode the significant statistics for the observable components associated with each cluster.

as a second data set, we used the OntoNotes English corpus (release 2.0), a more diverse resource that provides a larger coverage of event (and entity) annotations.


To overcome these limitations, we created two additional models.

one direct application of the mIBP model is to integrate it into the framework of the HDP extension model described in the previous section.


If we consider that each cell from the counting matrices associated with each feature type is represented into the memory by one byte, the total space required to store only one such matrix is, on average, 81,938×3,440 bytes.

this figure depicts the hierarchy of the events described in Example (1).


More recently, various other applications have been proposed to improve the existing HDP inference algorithms (Wang, Paisley, and Blei 2011;Bryant and Sudderth 2012).

because the number of event mentions in the ECB corpus is smaller than the number of mentions in the ACE corpus, the iFHMM-iHMM model utilizes a larger number of features values (0.91% of feature values selected for α ′ = 150) extracted from the new corpus in order to obtain most of its best results.


(a) Example of movement of the finite verb to the end of a dependent clause.

for the QPD features, we initialized the phrase dependency probability feature weights to 0.002 and the weights for all other features to 0.


Because of this we use a coarse-to-fine strategy for decoding (Charniak and Johnson 2005;Petrov 2009).

for example, when of the union is expressed in German, the preposition will often be dropped and the definite article chosen to express genitive case.


The bold words illustrate a "sibling" relationship, meaning that the source words aligned to the parent and child in the English sentence have the same parent on the German side.

for many real-world applications, such as translation, one of the sentences is provided.


At the beginning of the annotation task, we describe the aim of the annotation as shown in Figure A1 in the Appendix.

and quite late in the day, at 19:19 last night to be accurate, the Telegraph's external media adviser sent me a statement attributed to an unidentified "spokesman for the Daily Telegraph."


Assume the secret bitstring 1-bit Word 1-bit 2-bit Word We 0 complete the charitable 0 00 labor .

for example, both marry and wed appear in the two synsets in Table 1.


(2009) reported the data embedding rate of their system as 0.81 bits per sentence.

one of the major transformations used in linguistic steganography is synonym substitution.


Note that we only take single word substitution into consideration in order to avoid the confusion of finding information carriers during the secret recovering phase.

both NGM and NGM DVG assign a score to a word according to the context and the most likely word in the group of alternatives.


In this case, the translation-based transformation cannot work with block code encoding.

in addition, we are interested in the effect of the threshold value on the performance of the NGM method.


In other words, we do not investigate the document-level coherence of stego text because this requires sophisticated knowledge of natural language semantics and pragmatics which we consider to be outside the scope of this work.

the human annotation data comprises 2,010 sentences selected from the English Internet Corpus (Sharoff 2006), and consists of 201 target words: nouns, verbs, adjectives, and adverbs, each with ten sentences containing that word.


Note that the receiver does not need to know the original cover text.

the experimental setup follows a Latin square design (Kirk 2012) with three groups of 10 native English speakers as shown in table 10.


However, both bind and draw have only one neighbor, which means that only two codewords can be accommodated by these nodes, namely, bits 0 and 1.

the two-bit codeword assigning procedure first loops through all the nodes in Q 1 and then Q 2 .


The synset that includes the most positive substitutes is used to represent the meaning of the target word.

unlike the conclusion derived by Meng et al.


This means that the fundamental requirement of the coding method is satisfied: No matter what the target word is in the graph, any two-digit prefix of a secret message can be accommodated.

in Winstein's steganography scheme there is no method to filter out unacceptable substitutions so the generated stego text may be unnatural and arouse suspicion in others.


We define various linguistically motivated classification features and introduce novel ways for computing them.

off-line, we compute the same histograms for positive and negative examples and average them as before.


For each evaluation set (WordNet, DELA English, and DELA French), we divide the number of bigrams in the set that are classified as MWEs by the size of the intersection of the evaluation set with the monolingual corpus.

section 4 discusses the linguistically motivated features and their implementation; the organization of the Bayesian network is described in section 5.


We compute a histogram of the distribution in the corpus of all the possible surface forms of each MWE candidate.

we exclude from the evaluation those MWEs in the evaluation set that never occur in our corpora.


Because they are not free, it is difficult for small research groups to access them.

a global NER system could be developed from the union of all the optimized set of features for each NE type.


For example, if the size of the sliding window is 5, the decision on the targeted word is made based on its features as well as the features of its two immediate left and right neighbors (i.e., +/-2 words Abdallah, Shaalan, and Shoaib 2012).

an approach relying only on looking up entries in proper noun dictionaries would not be an appropriate way to tackle this problem, as ambiguous tokens/words that fall in this category are more likely to be used as non-proper nouns in text (Algahtani 2011).


With regard to the first aim, the book is very much a success: The writing is clear and concise, informative examples motivate each new topic, terminology is clearly defined, and descriptions of key algorithms are provided in the running text along with short (usually one-line) descriptions of each piece of relevant related work.

with regard to Liu's second aim of covering all important topics in the field, the degree to which the book succeeds is a matter of, well, opinion.


The remaining chapters are organized around this definition and the sentiment-based applications that it enables.

as such, it aims to be accessible to a broad audience that includes students, researchers, and practitioners, as well as to cover all important topics in the field.


Tree entropy takes into account not just the two highest ranked analyses, but all trees proposed by the parser for that sentence.

from this table, we can compute precision and recall of our sentence selector: 445 sentences were selected out of the 1,428 sentences in the test set (31.2%).


2005), and the best parse is manually selected.

tree entropy is a good measure for Baldridge and Osborne (2003), whose purpose is to select sentences where the model is less confident, but our simple baseline seems to be better when the goal is to select sentences where the first parse is the correct one.


We define the transition probability P t+1|t (j|i) from i to j by normalizing the weights of the edges out of node i, so: where k represents all nodes in the neighborhood of i. P t+1|t (j|i) denotes the transition probability from node i at step t to node j at time step t + 1.

this helps us find better related words that suit our task.


If the word w is positive then the probability that the random walk hits a positive word is higher, and if w is negative then the probability that the random walk hits a negative word is higher.

to overcome this limitation, we use a Web-based distributional approach to find the set of related words to each OOV word.


We also look at the performance of the proposed method for different parts of speech, and for different confidence levels.

being able to automatically identify the polarity of OOV words will essentially benefit real-world applications.


13 We train one classifier per predicate in the Keller and Lapata data set.

wilks (1978) gives the example My car drinks gasoline, which must be understood non-literally since car strongly violates the subject preference of drink and gasoline is also an unlikely candidate for something to drink.


In the interest of parsimony we leave this detail implicit in our notation.

in this article we have shown that the probabilistic latent-variable framework provides a flexible and effective toolbox for distributional modeling of lexical meaning and gives state-of-the-art results on a number of semantic prediction tasks.


As Turney observes, this approach seems to have the effect of underestimating model quality relative to the interannotator agreement figure, which is calculated as average intersubject correlation.

a further advantage of the probabilistic framework is that it is often straightforward to extend existing models to account for additional structure in the data, or to tie together parameters for shared statistical strength, while maintaining guarantees of well-normalized behavior thanks to the laws of probability.


Each verb-noun tuple was matched with two verbs that are synonyms of the reference verb in some contexts but not in Table 7 Most-and least-accurately predicted items for the LDA P→A models using per-item Spearman's ρ pseudo-coefficients on the unseen data set, with gold and predicted rank values.

in addition to Mitchell and Lapata's ρ cat evaluation, we also perform an evaluation that computes the average correlation ρ ave between the system output and each individual annotator: 4.4.2 Models.


To be able to characterize these changes we first examined the consistency of the role assignments created by the two algorithms.

in an attempt to reduce the sparsity of the argument fillers, they also present variants of the factored and coupled models where the argument heads have been replaced by lexical cluster ids stemming from Brown et al.


For example, the predicate break is transitive in sentences (1a) and (1b), and intransitive in sentence (1c).

in the unsupervised setting, we slightly reformulate argument identification as the task of discarding as many non-semantic arguments as possible.


Figure 3 plots the cluster numbers output by the systems against the estimate difference for ML vs. JK ( Figure 3a) and ML vs. BUB (Figure 3b).

one of the simplest schemes, with good numerical stability properties, is the recursive computation:P where j = 1, .


The most standard estimator is the maximum-likelihood (ML) estimator, which substitutes the probability of each event (cluster, classes, or cluster-class pair occurrence) with its normalized empirical frequency.

the fact that WSI systems do not rely on fixed inventories also makes it notoriously difficult to evaluate and compare their performance.


As an example of testing whether an RTN accepts a sequence, consider the RTN R of Figure 6 and the sequence x = a a b.

this is an approach that is widely used in automatic speech recognition (Ljolje, Pereira, and Riley 1999) and we note that it relies on efficient representation of very large search spaces t for subsequent rescoring, as is possible with FSAs and PDAs.


When two outgoing arcs have the same input symbols, the determinization algorithm chooses the arc with the lowest weight, 1, JJ .

the tropical semiring mentioned above is a common example of a semiring that observes the path property, since and therefore if w 1 < K w 2 , then w 1 ⊕ w 2 = w 1 , and vice versa.


Hence the state s l can always be reached from s i with a lower cost through state s j than by first taking the backoff arc from s i .

for example, a verb phrase in English could be represented as a NP\S, because when combined with an NP on the left, it makes an S. Similarly, a determiner is NP/N, because it combines with an N on the right to make an NP.


, namely, a lexicographic tuple over V. The number of elements of the tuple is the same as the number of constraints needed in the system being described.

the result is deterministic in the sense that for any input, there is a unique path through the lattice.


From their beginnings in the 1950s and 1960s to implement small hand-built grammars (e.g., Joshi and Hopely 1996) through their applications in computational morphology in the 1980s (Koskenniemi 1983), finite-state models are now routinely applied in areas ranging from parsing (Abney 1996), to machine translation (Bangalore and Riccardi 2001;de Gispert et al.

in M, because there is a transition leaving state s i labeled with w, the backoff arc, which is a failure transition, cannot be traversed, hence the destination of the n-gram arc s j will be the next state in p. in M , both the n-gram transition labeled with w and the backoff transition, now labeled with , can be traversed.


In some cases, however, the previous words provide additional useful information (e.g., for local agreement); hence there is value in taking both the semantic and word context into account whenever needed.

table 4 illustrates the 10 types of dialogue acts that are produced by the dialogue manager.


Each dialogue act contains an average of 3.27 mandatory semantic stacks.

naturalness is defined as whether the utterance could have been produced by a human.


Finally, Section 8 discusses the implication of these results as well as future work.

an utterance class is the set of utterances matching a specific dialogue act type and a set of zero or more slots.


More recent work has investigated other types of reranking models, such as hierarchical syntactic language models (Bangalore and Rambow 2000), discriminative models trained to replicate user ratings of utterance quality (Walker, Rambow, and Rogati 2002), or language models trained on speaker-specific corpora to model linguistic alignment (Isard, Brockmann, and Oberlander 2006).

see Table 4 in section 6 for more example dialogue act types.


The main advantage of data-driven methods over handcrafted methods is their potential for scaling to such large domains by shifting the bulk of the development effort from manual tuning to data collection.

is replaced by its stack head h t (step 3).


As the optimal back-off strategy can vary depending on the context, the realization model implements parallel back-off strategies (see steps 2, 4, and 5 in Figure 4(b))-that is, multiple back-off paths are explored at run-time, and the probability of each back-off node is computed as the maximum probability of all outgoing paths.

a major drawback of the utterancelevel overgenerate and rank approach is its inherent computational cost.


Lu, Ng, and Lee (2009) show that Tree Conditional Random Fields (CRFs) outperform WASP −1 and their own inverted semantic parser, based on automated evaluation metrics, although their system remains to be evaluated by human judges (Lu, Ng, and Lee 2009).

this requires that the meaning representation should be simple enough to be understood by untrained annotators.


The EFI method automatically generates feature templates for a structure learning problem by conjoining basic features that are highly discriminative.

the first sieve threshold is 4 for Arabic and Chinese and 8 for English.


Although the candidate pair generation step does not significantly impact the precision, this step is able to discard a large portion of the candidate arcs, as depicted in Section 8.3.

one key modeling aspect of our method consists of representing coreference clusters as latent trees.


Additionally, there are recall and precision errors due to errors in the automatically Table 16 Performances on the development sets per system step.

we also can see that when not using the averaging procedure, the algorithm eventually achieves the optimum performance, as occurs after the 25th epoch.


Basic Feature Setting -where we set basic features that indicate whether an arc is likely to be connecting a coreferent pair by adapting the features used by dos Santos and Carvalho (2011); Context Feature Induction -where we conjoin basic features to generate complex features with high discriminating power by means of the entropy-guided feature induction method proposed by  and Milidiú, dos Santos, and Duarte (2008); and Coreference Tree Learning -where we learn how to extract the trees that connect coreferent mentions in the graph of mentions by applying a large margin latent perceptron structure learning algorithm.

additionally, we see that our method seems to converge before 50 epochs, which is the number of epochs used to train all models used in this work.


The generation of these graphs for the CoNLL-2012 data sets is detailed in Section 7.2.


The Arabic data set does not contain named entity, semantic role labeling, and speaker annotations.

the most popular decision tree learning algorithms use the Gain Ratio to select the most informative feature (Quinlan 1992;Su and Zhang 2006).


From Table 3, it can be further observed that the best scores on Chinese and English are similar.

in Table 2, we describe the set of basic features used in our system.


ranking models are unable to exploit cluster-level features.


The objective function of this problem is given by s, and it scores the candidate clusterings for the given document.

yu and Joachims use undirected spanning trees instead of directed ones.


A tree score is given by a linear function over its features.

such cases are rare, given that the recall of the used sieves is approximately 90%.


Their method, the cluster-ranking model, ranks preceding clusters rather than candidate antecedents, thereby enabling the use of cluster-level features.

there is a trade-off between the restrictiveness of the sieves and the coreference resolution recall.


As a result of our similarity-based approach, we could generate as many pseudowords as polysemous nouns in WordNet 3.0 (i.e., 15,935 pseudowords).

we present in the table the representativeness scores given by each of our annotators to the individual pseudosenses of this word.


We showed that the similarity-based pseudowords are fairly close to their real counterparts in terms of disambiguation difficulty.

otrusina and Smrz (2010) exploit WordNet, whose conceptual units are synonym sets, called synsets, which encode the different meanings of words.


In order to be able to analyze the impact of the amount of knowledge on the disambiguation performance, the 800 sentences in the training data set were split into 10 different subsets of varying size (from 80 sentences [i.e., 10% of training instances] to the full set of 800 sentences in 10 steps) while at the same time preserving the original sense distribution for all these sets.

many of the WordNet synsets do not contain monosemous terms and the similarity-based approach often needs to look further into the other indirectly related synsets so as to find a suitable pseudosense.


For other entities in the same set, it learned to use adaptive choices.

if the user responds with an appropriate response to the system's instruction, K x is set to true.


This is evident from the knowledge profiles that (assuming different types of users are equally distributed) there is 0.66 probability that a user knows Ethernet light given that he or she knows Ethernet cable, and so on.

note that the Jargon-adapt and Switching-adapt policies exploit the user model in their subsequent references.


If so, annotating them again at the discourse level would have involved needless repetition.

a temporal event is annotated on the head of the syntactic construction that expresses it-the verb, in the case of a clause, as in the annotation of resume and warrant in Example 25, where the SIGNaL until is asserted to signal the temporal relation between resume and warrant.


Resources then limited which types of explicit linguistic signals of discourse relations were actually annotated as such.

this is the case in Example 28, where timeBank annotates the holding event as occurring before the adjusting event, which has a negative polarity attribute.


2001) realized either directly (i.e., via an anaphoric pronoun or NP in the second sentence) or indirectly (i.e., through a bridging inference).

rather than asking annotators to directly classify the sense of relations, which is a difficult task (Stede 2008), annotators were asked to look at lexical items that can signal discourse relations, such as the expression in Example (1).


Again, while these differences are admissible without impacting the annotation scheme in any major way, comparative studies using these corpora need to be sensitive to these differences.

marketers of faux gems steadily lost space in department stores to more fashionable rivals-cosmetics makers.


The choice of ESSK is motivated by its successful use in different NLP tasks in recent years Joty 2009, 2011;Hasan 2012a, 2012b).

our ESSK implementation considers the alternative of each word as its disambiguated sense.


Two approaches are often used to connect the words in different languages in the literature.

in the rest of this section, we will focus on how to build the inter-language relation.


The unifications performed by the independent adjunction of β 1 and β 2 to η o are the same as those that would be performed by a dependent adjunction of β 2 to β 1 and of the resulting derived tree to η o .

b. Richard Parker and Pi wandered the Algae Island yesterday.


We therefore propose a parsing algorithm that, given several auxiliary trees β 1 , .

as shown in Section 2.2, however, non-intersective modifiers arguably license a dependent derivation while some cases of multiple adjunction may involve both a dependent and an independent derivation.


The proposed algorithm simulates the established unification mechanism in FB-TAG without affecting the representation and the stipulations (e.g., null adjunction at the foot node and the bounded feature structures) of the grammar itself.

("No adjunction") unify the top and the bottom feature structures of nodes where no adjunction occurs, and Type 4(b) rules ("Start root of adjunction") unify the top feature (η.T) of the node (η) being adjoined to with the top feature structure (η r .T) of the root node (η r ) of the auxiliary tree being adjoined: 4 As shown in Figure 10, this approach can incorrectly lead to derivation failures in the case of an independent multiple adjunction.


Table A.1 shows the step-wise recognition of the string all the meerkats.

for instance, in the derivation of all the meerkats depicted in Appendix Figure A.2, the following rules will not be produced, thereby blocking the production of the dependent derivation.


The success of DSMs in essentially word-based tasks such as thesaurus extraction and construction (Grefenstette 1994;Curran 2004) invites an investigation into how DSMs can be applied to NLP and information retrieval (IR) tasks revolving around larger units of text, using semantic representations for phrases, sentences, or documents, constructed from lemma vectors.

in this experiment, we compare the best and worst performing models of Mitchell and Lapata (2008) to our own.


As expected from the fact that Multiply and Categorical differ only in how the verb vector is learned, the results of these two models are virtually identical, outperforming both baselines and the Additive model by a significant margin.

in Section 3.2 we showed how a pregroup algebra and vector spaces can be modeled as compact closed categories and how product categories allow us to relate the objects and morphisms of one category to those of another.


Thinking not only about how to address these aspects of language within compositional models, but how to evaluate them, should be a priority for all those interested in further developing this field.

for the same reason we considered Spearman's ρ to be a fair means of model comparisonnamely, in that it required no model score normalization procedure and thus was less likely to introduce error by adding such a degree of freedom-we consider the HIGH/LOW means to be inadequate grounds for comparison, precisely because it requires normalized model scores for comparison to be meaningful.


For dependency parsing, the dependency edges are grouped according to POS tag pairs.

annotation adaptation may give us a new perspective on knowledge induction and measurement for such methods.


Eschewing theorizing to stay close to data permits a remarkably wide range of linguistic phenomena to be covered, and it is this that is the book's greatest strength.

rather, it is a survey of the morphological and syntactic means by which different languages express meaning, anchored by clear and effective examples from typologically diverse languages.


The second most frequent proper name bigram in this corpus is found to be Falun Gong.

this book is, to the best of my knowledge, the first attempt at providing such an overview.


As a source, this corpus has the advantages of (1) being produced by a number of different people of different ages, educational attainment, and background; (2) being produced through a Web browser, without any help from spelling correction modules usually present in text editors; and (3) being freely available for download over the Web.

illustrative, Medeiros's findings suffer from a major drawback, to the extent that he relied on pre-existing lists of erroneous words in the related literature, along with handwritten errors made by high school students, with only part of the data coming from e-mail exchanges-that is, from actual typed text.


Let us discuss some useful properties of this mechanism: 1.

possible operations are (i) generation of a sequence of source and target words, (ii) insertion of gaps as explicit target positions for reordering operations, and (iii) forward and backward jump operations that do the actual reordering.


A simple answer is to add some lexicalized method for enforcing target restrictions to CCG, specifically on the application rules.

note that the category X 1 is taken from the lexicon, while every other category is derived by combining the preceding category on the path with some secondary input category (not on the path) by means of some combinatory rule.


With these two properties established, Theorem 4 can be proved by literally the same arguments as those that we gave in Section 3.

we prove that the full class of prefix-closed CCGs is weakly equivalent to TAG, and the subclass that cannot express target restrictions is strictly less powerful.


This shows that the rule system is prefix-closed.

we find that, at least with existing means, the weak equivalence result of Vijay-Shanker and Weir cannot be obtained for lexicalized CCG.


This is trivial: If there is a transitive FRG subgraph of G whose sum of edges ≥ α, then choosing the same edges will yield a transitive FRG subgraph of G whose sum of edges ≥ α.

the first rule was annotated in the rule application Others seek medical care ⇒ Others apply for medical care (Recall that crowdsourcing was used to annotate rule applications, and rules were extracted from these applications.)


Indeed, it seems that although performing node re-attachments monotonically improves the objective function value, it is not powerful enough to learn a graph that is better with respect to our gold standard.

one possible direction is to learn latent "sense" or "topic" variables for each rule (as suggested by Melamud et al.


This allows us to decompose the graph into smaller components in a way that guarantees that an exact solution for each one of the components results in an exact solution for Max-Trans-Graph.

the solution is clearly optimal because we obtain a maximal solution for a less-constrained problem that does not violate any transitivity constraint.


This is because the local score provided by the classifier for (encrypt, convert) is 0.997-slightly higher than the local score for (encrypt, encode), which is 0.995.

the transitive closure of a directed graph G is obtained by adding an edge from node i to node j if there is a path in G from i to j.


contrary to the typed graphs, untyped graphs that contain ambiguous predicates do not decompose well into small components.


We note that a trivial baseline would be to initialize edges based on whether s ij > λ and then compute the transitive closure in each component.

it might be possible to decompose entailment graphs into small components and solve each component separately.


On SST, s.parser outperforms SVM, MNB, and LM by 3.4 percentage points, 1.4 percentage points, and 3.8 percentage points, respectively.

in this section, we present how to learn the parameters of the polarity model for the combination rules.


Further, where the major empirical results of a paper are from automatic metrics, it is still useful to supplement them: As two examples, Collins, Koehn, and Kucerova (2005) and Lewis and Steedman (2013), in addition to a metric-based evaluation, present human judgments of preferences for their systems with respect to a baseline ( Fig.

equal the number of cell counts minus the number of model parameters; both deviance and d.f.


The final chapter concludes with a very strong statement: The mainstream of computational linguistics research supposedly concentrates on domain-independent semantic representations, which is deemed "wrong," as it would not do justice to domain distinctions and would not enable the connection to domain-specific ontologies to allow for domain-specific reasoning.

each concept is associated with an exhaustive enumeration of patterns it is expressed in.


The lower bound L(Λ) for the optimal covering cost is 7,689 ± 5 phones.

secondly, this work is a preliminary step to building a phonetically rich script before its recording in order to produce a high quality speech synthesis.


MUSHRA average ranks for 30 sentences from the Test corpus with 10 listeners per sentence (the lower the better).

on average over the 47 instances, LamSCP takes 390 (±9) times as long as ASA.


In order to reach the quality goal, the subgradient phase provides a near-optimal solutionΛ of the dual Lagrangian problem (8) using a subgradient LamSCP algorithm.

let us briefly recall the main principles of lagrangian relaxation on which lamSCP is based to solve Equation 2(see Fisher [1981] for more details on lagrangian relaxation).


In the case of the SCP with multirepresentation constraints, the definition of the contribution of s j ∈ X * can be adapted as follows: The second term of Equation (12) consists of sharing the contributionλ i (AX * − B) i of the excess instances of u i in X * according to the distribution of u i instances in X * .

moreover, the number of attributes to cover is lower: 1,207 2-phonemes in Le-monde and 2,012 in Gutenberg.


For example, Figure 7 demonstrates how we generate the training instances (right) from a gold DT with four EDUs (left).

in RST-DT, by our count, 7, 321 out of 7, 673 sentences in the training set, 951 out of 991 sentences in the test set, and 1, 114 out of 1, 208 sentences in the doubly-annotated set have a well-formed DT.


Arguably, to perform a complete rhetorical analysis, one needs to use supervised machine learning techniques based on human-annotated data.

first, the instructional corpus has a smaller amount of data with a larger set of relations (76 with nuclearity attached).


Rhetorical analysis seeks to uncover this coherence structure underneath the text; this has been shown to be beneficial for many Natural Language Processing (NLP) applications, including text summarization and compression (Marcu 2000b;Daumé and Marcu 2002;Sporleder and Lapata 2005;Louis, Joshi, and Nenkova 2010), text generation (Prasad et al.

the cost function for training the model is given by these two cascaded classifiers applied at different levels of the Dt.


The results of our k-best document-level parsing suggest that often the best tree is missing in the top k parses.

in Table 2, we list all constituents of the two DTs and their associated labels at the span, nuclei, and relation levels.


Table 3 shows the discourse segmentation results of different systems in Precision, Recall, and F-score on the two corpora.

if a sentence contains n EDUs, which corresponds to n − 1 intra-sentential segment boundaries, we measure the segmenter's ability to correctly identify these n − 1 boundaries.


We experiment with our full document-level discourse parser on the two corpora using the two parsing approaches described in Section 4.3, namely, 1S-1S and the sliding window.

13 SPADE does not come with a training module for its segmenter.


First, they do not differentiate between intra-and multi-sentential parsing, and both scenarios use a single uniform parsing model.

a well-written text has a coherence structure (Halliday and Hasan 1976;Hobbs 1979), which logically binds its clauses and sentences together to express a meaning as a whole.


Forward-backward on a sequence containing T units costs O(TM 2 ) time, where M is the number of relations in our relation set.

because such features did not improve segmentation accuracy on the development set, they were excluded from our final set of features.


6.1.4 Disagreements on Long versus Short Spans of Texts.

it is already compatible with annotations created with the Glozz Annotation Platform (Widlöcher and Mathet 2012), and with annotations generated by the Corpus Shuffling Tool.


It is necessary here to choose once and for all what unit to compare to what other, rather than to perform all the comparisons.

for a thorough introduction to the most popular measures that concern categorizing, we refer the reader to the excellent survey by Artstein and Poesio (2008).


Positional discrepancies considered as category mistakes or not by c|u α and γ.

most popular measures are chance-corrected: S (Bennett, Alpert, and Goldstein 1954) relies on a uniform distribution model of chance, π (Scott 1955) and α (Krippendorff 1980) on the mean distribution, and κ (Cohen 1960) on individual distributions.


Positional and Categorial Permissiveness of γ.

this alignment indicates for unit u a 1 i of annotator a 1 , to which unit u a 2 j of annotator a 2 it corresponds, in order to compute the associated dissimilarity.


However, SEARN is more oriented to greedy search, optimizing local decisions.

the original pruned gold-standard edge is treated as the positive example for the update.


Putting such new edges on the agenda will result in the system keeping multiple edges with the same signature.

example string with its CCG derivation, used to give example features in Table 1.


In this article, however, our chart is a single beam structure containing the top-scored accepted edges.

for the best-first search decoder, the model is used to order hypotheses on the agenda, which can cover different numbers of words.


Numerically, they are similar to the border-split cross-validation results, but again with a different ranking of the feature combinations.

the size of the test data set was the same as in time-split (below) but the size of the training data set was 5,868. r time-split validation: We chose the cut-off time t * so that 90% of the data was used as training (5,922 tweets) and 10% as validation (658 tweets).


We note that, as we do not have access to the data sets used in these studies, we cannot verify the level of influence that the data dependence and their choice of evaluation have on their reported results.

time-split and time-border-split Validation.


2013;Li, Zhu, and Wang 2013;Neuman et al.

here, the domain-interaction and abstractness-based methods are leading, with the clustering-based method coming third.


The existence of this association allows us to transfer knowledge and inferences from the domain of mechanisms to that of political systems.

for example, the accuracy of 95% reported by Gedigian et al.


In initial tests, these vectors performed better than binary vectors straightforwardly derived from features of the word in context.

ideally it should not rely on any domain-specific information or focus on individual types of instances (e.g., a limited set of hand-chosen source-target domain mappings).


Based on such alignments, we show that implicit argument instances can be automatically induced and applied to improve a current model of linking implicit arguments in discourse.

in addition to entity-grid like features, Pitler and Nenkova (2008) also use text length, word overlap, and pronoun occurrences as features for predicting readability.


15 We apply our own model on each data point in the small annotated test set, where we always treat the affected argument, regardless of its actual annotation, as implicit to extract unbiased feature values for classification.

in this chapter, we presented a computational linguistic application of the automatically induced data set of implicit arguments that we introduced in Section 5.


[  In this section, we introduced a computational implementation of Step 2 of our rulebased induction method for identifying implicit arguments and their discourse antecedents.

we developed a framework for automatically inducing instances of implicit arguments, and we designed a novel coherence model that predicts the effect of argument realizations on perceived textual coherence.


8 To facilitate the evaluation of models for both concrete and abstract concept meaning, and in light of the cognitive and computational modeling differences between abstract and concrete concepts noted in Section 2.2, we aimed to include both concept types in SimLex-999.

indicated by the difference between the red and gray bars), suggesting that the improvement is driven at least in part by an increased ability to "distinguish" similarity from association.


Association contrasts with similarity, the relation connecting cup and mug (Tversky 1977).

we finish by reviewing existing gold standards, and show that none enables a satisfactory evaluation of the capability of models to capture similarity (2.3).


However, as shown in the following section, a model need not be an exemplary model of similarity in order to perform well on WS-Sim, because an important class of concept pair (associated but not similar entities) is not represented in this data set.

(2009) as either containing similar or unrelated (neither similar nor associated) concepts.


This is achieved by stochastic gradient descent.

the true explanation may be less simple, since the Huang et al.


Regardless of cause, however, the high agreement on adjectives is a satisfactory property of SimLex-999.

we applied the measure of Wu and Palmer (1994) (henceforth WupSim), which approximates similarity on a [0,1] scale reflecting the minimum distance between any two synsets of two given concepts in WordNet.


These algorithms are treated in a non-probabilistic manner and are illustrated extensively in Prolog.

the chapter mainly focuses on European languages, but I believe additional discussions on other language families would be helpful.


However, I would encourage everyone to think about problems, architectures, cognitive science, and the details of human language, how it is learned, processed, and how it changes, rather than just chasing state-of-the-art numbers on a benchmark task.

(Jane Austen, Emma, 1816) ( 3) The difficulty is in the getting the gold into Erewhon.


I started my research, however, not from machine translation but from information retrieval (IR).

after that, we have also established joint labs with other companies, like IBM, Baidu, and Tencent.


In those years, the translation from Chinese to other foreign languages was less studied in China.

although we didn't work together, we could always learn from each other and inspire each other in MT research.


He is a strong supporter of the idea of sharing.


Other people all thought it was impossible and laughed at him.

as more and more work was conducted, I found it more difficult to go deeper.


SGD-style online gradient-based methods have been used in translation for optimizing risk-based (Gao and He 2013), ranking-based (Watanabe 2012;Green et al.

we can assume that the algorithms being used outside of the context of research on optimization itself are those that satisfy these criteria in some way.


This has the effect of decreasing the variance, and thus the learning rate, for frequently updated features, preventing large and aggressive moves of weights for features that already have been seen often and thus can be considered relatively reliable.

2013) in which the optimization is performed in two steps: (t) ,Ẽ (t) ,C (t) ; w (t) ) (71) First, we perform updates without considering the regularization term in Equation 71.


Converting this to a loss function that is dependent on the model parameters, we obtain the following loss expressing the error over the 1-best results obtained by decoding in Equation 1: Error has the advantage of being simple, easy to explain, and directly related to translation performance, and these features make it perhaps the most commonly used loss in current machine translation systems.

it is also possible to perform leaving-one-out training, where counts of rules extracted from a particular sentence are subtracted from the model before translating the sentence (Wuebker, Mauser, and Ney 2010).


In the batch learning methods of Section 5, the steps of decoding and optimization are performed sequentially over the entire training data.

corpus-level measures are calculated by taking statistics over the whole corpus, whereas sentence-level measures are calculated by measuring sentence-level accuracy, and defining the corpus-level accuracy as the average of the sentence-level accuracies.


Compared with online learning, within the batch optimization framework, parallelization is usually straightforward.

large and sparse feature sets make the MT optimization problem significantly harder, and many of the optimization methods we will cover in the rest of this survey are aimed at optimizing rich feature sets.


Evaluation Measures for Optimization: Although many evaluation measures show consistent improvements in correlation with human evaluation scores over BLEU when used to evaluate the output of existing MT systems, there are few results that show that systems optimized with evaluation measures other than BLEU achieve consistent improvements in human evaluation scores.

as mentioned in Section 2.5, evaluation of MT is an active research field, and there are many alternatives in addition to BLEU.


Given a sentence of length n and the word of index i, i will represent the word as an argument and i + n will represent the word as a predicate.

we have used this error analysis to define the following four SC patterns, which we call SBJ, OBJ, VdeN, and VaN: The last column of Table 2 indicates the SC pattern that corresponds to a given type of error.


9 Intuitively, WORDS should yield zero recall.

2009;Kurimo, Virpioja, and Turunen 2010).


One important measure to be evaluated when assessing the performance of the interlaced update rule is the convergence rate of the EM algorithm.

in addition to this, weight adjustment can be performed offline, since typical MERT procedures use closed development corpora of a few thousand sentences (i.e., the training data is bounded and relatively small).


To solve this problem, the incremental view of the EM algorithm (Neal and Hinton 1998) can be applied.

in the following sections, we present the set of sufficient statistics for each model.


In the following sections, we present the set of sufficient statistics for each model.

modifying parameters may also produce lateral effects.


Under these circumstances, we compared the performance of a conventional SMT system with that of an online SMT system.

the improvements were not statistically significant in some cases (WER for English to French, BLEU and WER for English to German).


The different plots show the evolution of the user effort in the PE and IMT scenarios measured in terms of cumulative WER and KSMR, respectively.

the new update rule allows us to execute more than one training epoch over the incoming data while maintaining constant time and spatial complexity (see Sections 3 and 4.4 as well as Appendix A for more details).


The system was initialized with empty models, and after that such models were extended from the user validated translations using three different update frequencies: every 10, 100, and 1,000 sentences.

the search is restricted to generate target sentences compatible with the user prefix.


For the purpose of describing word order differences between language pairs, we have divided the features into two broad categories: clause-level and phrase-level.

the success of these approaches largely depends on the degree of isomorphism of the modeled language pair, as well as on the parser's performance, which can vary substantially across languages.


Then, a binary parsing model is trained to maximize the likelihood of source trees that can generate such reorderings.

decisions are only partially governed by the translation model, treelet-based SMT benefits from additional model components specifically dedicated to reordering.


For instance, in Figure 2, a monotonic phrase segmentation could be achieved only by treating the large block [jdd ... AlsAds]-[The ... renewed] as a single tuple.

with reference to Table 2, the permutation trees of both hypotheses (a) and (b) would contain only one swapping node leading to the same reordering score.


Syntax is often regarded as the most effective way to inform reordering in translation.

this parameter is typically set to 10 or 15 words, as wider spans result in prohibitively slow decoding and lower translation quality.


For instance, the swap of Alr}ys Alfrnsy may only be dictated by a rule applying to noun phrases composed of a noun and an adjective.

finch and Sumita obtain best results on a wide range of language pairs by combining the outputs of standard and reverse decoding systems.


(2011), a baseline treebank-trained parser is used to produce n-best parses of a parallel corpus's source side.

no distinction is made between short and long-range reordering errors.


To see this, let us assume that some connector vertex of G i is found inside the tree below a i .

we then replace each edge in M with a vertex in D, and replace each vertex in M with a clique in D. D is the dependency graph for an SCFG rule, because it has a vertex for each boundary, and a clique that connects the boundaries of each nonterminal (including the left-hand side nonterminal).


At each arc a of T M s ,τ s , the right-hand side of Equation 4corresponds to the right-hand side of Equation 2, and thus half the carving width of T M s ,τ s is equal to the fan-out of τ s .

the cycle follows a depthfirst traversal of G specified by Algorithm 1 in Section 4.2.1.


For technical reasons, grid X 1 needs a special treatment: the last edge (i 2m , i 2m+1 ) in γ is used to enter grid X 1 for the first time, and is therefore placed at vertex x 11,1 .

each clique has three other vertices, for a total degree of 6.


The green path follows a symmetrical pattern in visiting G i one column at a time, with a switch in the middle of column 2k.

theorem 3 suggests that designing a polynomial time algorithm for finding the parsing strategy of an SCFG rule having optimal time complexity is a difficult task.


Then nc s (l) measures the average number of CLIQUES to which the sentences for a given lemma are assigned.

all the notions of clusterability that ackerman and Ben-David consider are based on k-means and involve optimum clusterings for a fixed k. We consider three measures of clusterability that all assume a k-means clustering.


We built all these monolingual and bilingual data sets from texts we crawled from the Internet.

table 5 shows the performance of the three bitext combination strategies (see Section 4.4 for details) when applied to combine IN2EN with the original ML2EN (i), and with various adapted versions of ML2EN (ii-iv).


Most of the world languages are resource-poor for statistical machine translation; still, many of them are actually related to some resource-rich language.

we filter out translation pairs whose probabilities (Equation (1)) are lower than some threshold (tuned on the development data set), for example, 0.01.


Even if only one language pair l, r is considered, a document with 40 tokens would require approximately 7 million operations in the worst case for φ T to be computed for all segmentations.

it is also important to note that mainstream translation systems, such as Google Translate 11 and Bing Translator 12 fail to translate these examples.


Given that this update is linear and we need to iterate through all tokens twice, this update also takes O(n 3 ) operations in total.

then, each parallel sentence is extracted and placed in the set D s,t = {(s 1 , t 1 ), .


The corpora that were collected and the annotated gold standards are available at http://www.cs.cmu.edu/~lingwang/microtopia/.

this operation is only expensive if we compute the translation score, as the span and language scores can be more efficiently computed.


∆ c PMI(ab) equals the deviation of the actual PMI(ab, c) from the additive ideal, which any vector composition model is essentially trying to estimate.

the tendency for phrases to have lower PMI than predicted by the additive idealization is quite robust.


This connects the PMI of a phrase (based on counts of ab linked by a syntactic relation) to the PMI of the constituents (based on counts of the constituents in all contexts).

under this null hypothesis, addition should accurately predict PMI values for phrases.


This result highlights the effectiveness of the lightweight, transitionbased approach.

another concern of our work is about the model diversity obtained by the heterogeneous design of transition systems for general graph spanning.


To capture syntagmatic relations among words, a trivial idea is to use higher order Markov models.

this idea to use unlabeled data to transfer the predictive power of one model to another has been investigated in many areas, for example, from high accuracy neural networks to more interpretable decision trees (Craven 1996), from high accuracy ensembles to faster and more compact neural networks (Bucila, Caruana, and Niculescu-Mizil 2006), from structured prediction models to local classification models (Liang, Daumé, and Klein 2008), or from complicated parsing models to simpler ones .


If Step 2e is satisfied for A, i, and j, then add (A, i, j) to [T 3 ] ij .

during each multiplication, entries created in the product matrix correspond either to the application of an LCFRS rule with l.h.s.


With T being the seed matrix, we define where T (i) is defined recursively as: Under Condition 4.1, one can show that given an LCFRS derivation tree t over the input string, each node in t must appear in the transitive closure matrix T + .

as such, the total complexity is O(tn ωd ), where t is the number of iterations required to converge.


Any SCFG can be binarized into an LCFRS grammar.

production rules with LIGs copy the stack on the left-hand side to one of the nonterminal stacks in the right-hand side, 5 potentially pushing or popping one symbol in the new copy of the stack.


besides these kernel-specific settings, we configured the other hyperparameters as follows: -We used the soft margin method to allow training errors when constructing the decision boundary, and vary the associated cost parameter C between 2 −6 and 2 12 , stepping by a factor of 4 (default = 1).


In their gold-standard form, we observe that more of these features reveal a correlation with our data set.

2013) and Dutch (Frog; van den Bosch et al.


On the one hand we wished to identify whether it is possible to build an automatic readability prediction system that can score and compare the readability of English and Dutch generic text.

to decide which individuals will survive into the next generation, a selection criterion is applied defining how good the individual is at solving the problem-its fitness.


The actual number of features generated from this corpus using 5-9 p-grams is precisely m = 4, 662, 520.

applying these models to other genres of text (tweets, for example) is a challenge that has not been explored in this work.


For example, short vowels can be omitted from the Arabic writing, and consequently produce shorter words.


To find which of these two hypotheses is responsible for the performance gain, the presence bits kernel was computed on the raw text documents using a range of 5-8 p-grams.

preliminary results showed that trying to fit more of the training data leads to lower accuracy rates (not more than 1-2 percentage points, though) in the CV procedure.


However, an interesting remark is that such features can implicitly be discovered within the extracted p-grams.

this fact is also known as the kernel trick (Shawe-taylor and Cristianini 2004).


Furthermore, the LOO procedure can easily be performed on a small data set, such as the subset of 329 samples of the ALC.

the results on the ICLEv2 corpus using a 5-fold cross-validation procedure are summarized in table 6.


In the string kernels approach proposed in this work, documents or essays from the corpora are treated as strings.

it must be mentioned the results of string kernels are only 2 percentage points lower when the 10-fold CV procedure is used, but any further details of that experiment are not reported here.


Each language was separately considered to determine the optimal range.

it should also be pointed out that the corpora available for these kinds of studies are typically academic in nature (i.e., from language learners).


Kernel-based learning algorithms work by embedding the data into a Hilbert feature space, and searching for linear relations in that space.

the intersection kernel captures something more about the correlation between the p-gram frequencies in the two strings.


Universal quantification can be represented in AMRs, by multiple use of the polarity relation.

in order to deal with projection phenomena and quantifier scope the AMR language needs to be extended.


CAT was developed in the 1970s and has its roots in the field of social psychology.

many sociolinguists, together with scholars from the social sciences in general, view gender as a social construct, emphasizing that gendered behavior is a result of social conventions rather than inherent biological characteristics.


Another approach that would remove the need to define age categories is the unsupervised induction of age categories.

for example, work on predicting social variables such as gender (Section 3) is built on the idea that gender determines the language use of speakers.


A separate branch has also emerged, referred to as dialectometry (Wieling and Nerbonne 2015).

hovy (2015) found that training gender-or age-specific word embeddings improved tasks such as sentiment analysis and topic classification.


Sparse models, such as used in Eisenstein, Smith, and Xing (2011), that identify small sets of predictive features would be more suited for exploratory analysis.

crowdsourcing has already been used to obtain perception data for sociolinguistic research-for example, to study how English utterances are perceived differently across language communities (Makatchev and Simmons 2011) and to obtain native-likeness ratings of speech samples (Wieling et al.


Sociolinguists are primarily interested in new insights about language use.

although at some deep level there is much in common between the goals and values of the two communities, the differences in values signified by the emphasis on creativity and predictive accuracy on the one side and reliability and validity on the other side nevertheless pose challenges for mutual exchange.


Data sets derived from uncontrolled settings such as social media often lack explicit information regarding the identity of users, such as their gender, age, or location.

as we have highlighted in the methodology discussion (Section 2), the differences in values between the communities can be perceived as a divide.


Going forward, we need to appreciate where our work stands along an important continuum that represents a fundamental tension in the social sciences: qualitative approaches that seek to preserve the complexity of the phenomena of interest, versus quantitative approaches that discretize (but thereby also simplify) the phenomena to achieve more generalizability.

people who use names that are more common for a different gender will be incorrectly labeled in these cases.


Studies based on external power structures, such as the organizational structure of a company, treat power relations as static.

their value for practical data exploration tasks is therefore often limited.


Opinion summarization is quite different from conventional single-document or multidocument summarization, because it is centered on opinion targets and produces quantitative sentiment ratings for targets.

zhao's e-mail address is jzhao@nlpr.ia.ac.cn.


My own research showed that language users can match the probabilities of linguistic features of the environment and they have powerful predictive capabilities that enable them to anticipate the variable linguistic choices of others.

the facts suggest that these constraints play a role in syntactic typology and should not be brushed off as external to grammar and out of bounds to the theoretical linguist.


We define the informativity of a host-verb bigram B as in Equation 5: where B is the bigram consisting of a host and a verb in either contracted or uncontracted form (for example, blood's or blood is), N is the total frequency of B, and next i is the following context word for the i th occurrence of B in the corpus.

the working hypothesis I have adopted contrasts strongly with that of the garden: Grammar itself is inherently variable and probabilistic in nature, rather than categorical and algebraic.